AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This experience paper introduces a property-based testing approach for WHRs, finding 21 previously unknown bugs, including axis limit violations, entanglement issues, and missing interlock checks, and distilled a number of practical lessons learned.
LLM agents that invoke external tools face critical safety vulnerabilities when malicious manipulations exploit their implicit trust in tool outputs and metadata. However, identifying these vulnerabilities through testing is challenging due to the need to bypass safety guardrails with semantically legitimate inputs, th...
Kea2, a practical property-based testing tool for apps, is introduced, specifying properties in Python with enough flexibility and expressiveness; and reusing existing GUI fuzzing techniques to support effective property checking.
Deep learning (DL) compilers such as Apache TVM translate high-level models into optimized low-level code through multi-stage compilation pipelines. While recent testing efforts have improved fuzzing of optimization stages, they still face two key challenges: (i) the lack of semantics-preserving test models, leading to...
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